3-D volume imaging can be a valuable diagnostic tool that offers significant advantages over earlier 2-D radiographic imaging techniques for evaluating the condition of teeth, bones, and other internal structures and organs. 3-D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession.
Cone beam computed tomography (CBCT) or cone beam CT technology offers considerable promise as one type of diagnostic tool for providing 3-D volume images. Cone beam X-ray scanners are used to produce 3-D images of medical and dental patients for the purposes of diagnosis, treatment planning, computer aided surgery, etc. Cone beam CT systems capture volume data sets by using a high frame rate flat panel digital radiography (DR) detector and an x-ray source, typically affixed to a gantry that revolves about the subject to be imaged. The CT system directs, from various points along its orbit around the subject, a divergent cone beam of x-rays through the subject and to the detector. The CBCT system captures projection images throughout the source-detector orbit, for example, with one 2-D projection image at every degree increment of rotation. The projections are then reconstructed into a 3-D volume image using various techniques. Among the most common methods for reconstructing the 3-D volume image from 2-D projections are filtered back projection (FBP) and Feldkamp-Davis-Kress (FDK) approaches.
Although 3-D images of diagnostic quality can be generated using CBCT systems and technology, a number of technical challenges remain. Highly dense objects, such as metallic implants, appliances, surgical clips and staples, dental fillings, and the like can cause various image artifacts that can obscure useful information about the imaged tissue. Dense objects, having a high atomic number, attenuate X-rays in the diagnostic energy range much more strongly than do soft tissue or bone features, so that far fewer photons reach the imaging detector through these objects. For 3-D imaging, the image artifacts that can be generated by metallic and other highly dense objects include dark and bright streaks that spread across the entire reconstructed image. Such artifacts can be due to physical effects such as high noise, radiation scatter, beam hardening, the exponential edge-gradient effect, aliasing, and clipping, and non-linear amplification in FBP or other reconstruction methods. The image degradation commonly takes the form of light and dark streaks in soft tissue and dark bands around and between highly attenuating objects. These image degradations are commonly referred to as artifacts because they are a result of the image reconstruction process and only exist in the image, not in the scanned object. These artifacts not only conceal the true content of the object, but can be mistaken for structures in the object. Artifacts of this type can reduce image quality by masking other structures, not only in the immediate vicinity of the dense object, but also throughout the entire image. At worst, this can falsify CT values and even make it difficult or impossible to use the reconstructed image effectively in assessing patient condition or for planning suitable treatment.
A number of approaches have been tried for metal artifacts reduction (MAR), with varying success and some shortcomings. Among the basic types of approaches that have been used are the following:                1. Interpolation-based FBP reconstruction approach. This approach operates in the projection domain, where the metal shadow is identified and obscured values are interpolated or in-painted using the data values of nonmetal-contaminated neighboring region. For some types of imaging, with a single metal object within a relatively homogeneous volume, this method works acceptably. However, in more complex heterogeneous tissue, particularly where there are multiple metal objects in a heterogeneous volume, the interpolation-based algorithm can make unrealistic assumptions about the region that lies in the shadow of the metal, leading to pronounced errors in the reconstructed images. It is generally held, in the 3-D imaging arts, that interpolation-based repair of the projections is based on a weak underlying model. Hence, it cannot be expected that the estimated projection data will suitably fit the projection data measured without metal objects.        2. Iterative reconstruction approach. Generally improved over the performance of interpolation-based FBP of approach 1, the iterative reconstruction approach uses the regions of the projections that are not contaminated by metal and other highly attenuating material in the reconstruction. The 3-D image is iteratively updated in order to converge on an image that, upon forward projection, results in calculated projections that closely match the measured projections outside of the metal shadow. A drawback of this approach is that the metal in the scanned object does not appear in the reconstruction and has to be added to the 3-D image in a subsequent step. Also, the reconstructed 3-D image is improved if, in the reconstruction process, the metal shadow regions of the projections are restored instead of neglected.        3. Prior image approach. In the prior image approach an initial reconstructed 3-D image is created. This is followed by a step in which metal artifacts are removed from the initial reconstruction to create a “prior” image. This prior image cannot serve as the final metal artifact reduced 3-D image because in the artifact reduction step significant image content is also removed. The prior image is used for the purpose of producing calculated projections which are merged into the measured projections in the metal shadow regions. The resultant composite projection images are then used to reconstruct the final artifact reduced 3-D image.        
Dental volume imaging can be particularly challenging because of the relative complexity of structures and shapes and because objects of very different densities are closely packed together in a relative small space. Various types of fillings, implants, crowns, and prosthetic devices of different materials can be encountered during the scan. Beam hardening effects can also impact image quality. Thus, metal artifacts reduction can be particularly difficult for dental volume imaging.
The reduction of artifacts that are caused by metal and other highly attenuating objects is valued for a number of reasons, particularly with the use of implants is growing in medical and dental treatments. Although some progress has been made to form volume image data that distinguishes features of different densities, there is still considerable room for improvement and a need for a method of metal artifacts reduction that offers improved performance and computational efficiency.